Script to reproduce years based on a model trained with random points¶
Importing¶
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import xarray as xr
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import TransformedTargetRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.metrics import root_mean_squared_error as rmse
from tqdm import tqdm
import dill
import random
import salishsea_tools.viz_tools as sa_vi
Datasets Preparation¶
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def datasets_preparation(dataset, dataset2):
drivers = np.stack([np.ravel(dataset['Temperature_(0m-15m)']),
np.ravel(dataset['Temperature_(15m-100m)']),
np.ravel(dataset['Salinity_(0m-15m)']),
np.ravel(dataset['Salinity_(15m-100m)']),
np.ravel(dataset2['Summation_of_solar_radiation']),
np.ravel(dataset2['Mean_wind_speed'])
])
indx = np.where(~np.isnan(drivers).any(axis=0))
drivers = drivers[:,indx[0]]
diat = np.ravel(dataset['Diatom'])
diat = diat[indx[0]]
return(drivers, diat, indx)
Regressor¶
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def regressor (inputs, targets):
inputs = inputs.transpose()
# Regressor
X_train, _, y_train, _ = train_test_split(inputs, targets, train_size=0.35)
model = ExtraTreesRegressor()
model = make_pipeline(StandardScaler(), model)
regr = BaggingRegressor(model, n_estimators=12, n_jobs=4).fit(X_train, y_train)
return (regr)
Regressor 2¶
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def regressor2 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
m = scatter_plot(targets, outputs_test, variable_name)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 3¶
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def regressor3 (inputs, targets):
inputs2 = inputs.transpose()
outputs_test = regr.predict(inputs2)
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs_test, deg=1)
r = np.round(np.corrcoef(targets, outputs_test)[0][1],3)
rms = rmse(targets, outputs_test)
return (r, rms, m)
Regressor 4¶
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def regressor4 (inputs, targets, variable_name):
inputs2 = inputs.transpose()
outputs = regr.predict(inputs2)
# Post processing
indx2 = np.full((len(diat_i.y)*len(diat_i.x)),np.nan)
indx2[indx[0]] = outputs
model = np.reshape(indx2,(len(diat_i.y),len(diat_i.x)))
m = scatter_plot(targets, outputs, variable_name + str(dates[i].date()))
# Preparation of the dataarray
model = xr.DataArray(model,
coords = {'y': diat_i.y, 'x': diat_i.x},
dims = ['y','x'],
attrs=dict( long_name = variable_name + "Concentration",
units="mmol m-2"),)
plotting3(targets, model, diat_i, variable_name)
Printing¶
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def printing (targets, outputs, m):
print ('The amount of data points is', outputs.size)
print ('The slope of the best fitting line is ', np.round(m,3))
print ('The correlation coefficient is:', np.round(np.corrcoef(targets, outputs)[0][1],3))
print (' The mean square error is:', rmse(targets,outputs))
Scatter Plot¶
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def scatter_plot(targets, outputs, variable_name):
# compute slope m and intercept b
m, b = np.polyfit(targets, outputs, deg=1)
printing(targets, outputs, m)
fig, ax = plt.subplots(2, figsize=(5,10), layout='constrained')
ax[0].scatter(targets,outputs, alpha = 0.2, s = 10)
lims = [np.min([ax[0].get_xlim(), ax[0].get_ylim()]),
np.max([ax[0].get_xlim(), ax[0].get_ylim()])]
# plot fitted y = m*x + b
ax[0].axline(xy1=(0, b), slope=m, color='r')
ax[0].set_xlabel('targets')
ax[0].set_ylabel('outputs')
ax[0].set_xlim(lims)
ax[0].set_ylim(lims)
ax[0].set_aspect('equal')
ax[0].plot(lims, lims,linestyle = '--',color = 'k')
h = ax[1].hist2d(targets,outputs, bins=100, cmap='jet',
range=[lims,lims], cmin=0.1, norm='log')
ax[1].plot(lims, lims,linestyle = '--',color = 'k')
# plot fitted y = m*x + b
ax[1].axline(xy1=(0, b), slope=m, color='r')
ax[1].set_xlabel('targets')
ax[1].set_ylabel('outputs')
ax[1].set_aspect('equal')
fig.colorbar(h[3],ax=ax[1], location='bottom')
fig.suptitle(variable_name)
plt.show()
return (m)
Plotting¶
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def plotting(variable, name):
plt.plot(years,variable, marker = '.', linestyle = '')
plt.xlabel('Years')
plt.ylabel(name)
plt.show()
Plotting 2¶
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def plotting2(variable,title):
fig, ax = plt.subplots()
scatter= ax.scatter(dates,variable, marker='.', c=pd.DatetimeIndex(dates).month)
ax.legend(handles=scatter.legend_elements()[0], labels=['February','March','April'])
fig.suptitle('Daily ' + title + ' (15 Feb - 30 Apr)')
fig.show()
Plotting 3¶
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def plotting3(targets, model, variable, variable_name):
fig, ax = plt.subplots(2,2, figsize = (10,15))
cmap = plt.get_cmap('cubehelix')
cmap.set_bad('gray')
variable.plot(ax=ax[0,0], cmap=cmap, vmin = targets.min(), vmax =targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
model.plot(ax=ax[0,1], cmap=cmap, vmin = targets.min(), vmax = targets.max(), cbar_kwargs={'label': variable_name + ' Concentration [mmol m-2]'})
((variable-model) / variable * 100).plot(ax=ax[1,0], cmap=cmap, cbar_kwargs={'label': variable_name + ' Concentration [percentage]'})
plt.subplots_adjust(left=0.1,
bottom=0.1,
right=0.95,
top=0.95,
wspace=0.35,
hspace=0.35)
sa_vi.set_aspect(ax[0,0])
sa_vi.set_aspect(ax[0,1])
sa_vi.set_aspect(ax[1,0])
ax[0,0].title.set_text(variable_name + ' (targets)')
ax[0,1].title.set_text(variable_name + ' (outputs)')
ax[1,0].title.set_text('targets - outputs')
ax[1,1].axis('off')
fig.suptitle(str(dates[i].date()))
plt.show()
Training (Random Points)¶
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ds = xr.open_dataset('/data/ibougoudis/MOAD/files/integrated_model_var_old.nc')
ds2 = xr.open_dataset('/data/ibougoudis/MOAD/files/external_inputs.nc')
ds = ds.isel(time_counter = (np.arange(0, len(ds.time_counter),2)),
y=(np.arange(ds.y[0], ds.y[-1], 5)),
x=(np.arange(ds.x[0], ds.x[-1], 5)))
ds2 = ds2.isel(time_counter = (np.arange(0, len(ds2.time_counter),2)),
y=(np.arange(ds2.y[0], ds2.y[-1], 5)),
x=(np.arange(ds2.x[0], ds2.x[-1], 5)))
dates = pd.DatetimeIndex(ds['time_counter'].values)
drivers, diat, _ = datasets_preparation(ds, ds2)
regr = regressor(drivers, diat)
Other Years (Anually)¶
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years = range (2007,2024)
r_all = []
rms_all = []
slope_all = []
for year in tqdm(range (2007,2024)):
dataset = ds.sel(time_counter=str(year))
dataset2 = ds2.sel(time_counter=str(year))
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor2(drivers, diat, 'Diatom ' + str(year))
r_all.append(r)
rms_all.append(rms)
slope_all.append(m)
plotting(np.transpose(r_all), 'Correlation Coefficient')
plotting(np.transpose(rms_all), 'Root Mean Square Error')
plotting (np.transpose(slope_all), 'Slope of the best fitting line')
0%| | 0/17 [00:00<?, ?it/s]
The amount of data points is 70794 The slope of the best fitting line is 0.738 The correlation coefficient is: 0.918 The mean square error is: 0.06581317458970341
6%|▌ | 1/17 [01:16<20:22, 76.39s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.729 The correlation coefficient is: 0.883 The mean square error is: 0.06867923153926321
12%|█▏ | 2/17 [02:27<18:16, 73.09s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.756 The correlation coefficient is: 0.927 The mean square error is: 0.07751808111193076
18%|█▊ | 3/17 [03:36<16:38, 71.35s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.726 The correlation coefficient is: 0.915 The mean square error is: 0.0619161166591015
24%|██▎ | 4/17 [04:43<15:04, 69.54s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.806 The correlation coefficient is: 0.932 The mean square error is: 0.05813325524217016
29%|██▉ | 5/17 [05:50<13:42, 68.56s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.777 The correlation coefficient is: 0.931 The mean square error is: 0.060037348624317505
35%|███▌ | 6/17 [06:55<12:24, 67.65s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.718 The correlation coefficient is: 0.915 The mean square error is: 0.07707629395431721
41%|████ | 7/17 [08:02<11:12, 67.29s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.739 The correlation coefficient is: 0.895 The mean square error is: 0.06589118551422224
47%|████▋ | 8/17 [09:08<10:01, 66.82s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.741 The correlation coefficient is: 0.926 The mean square error is: 0.06057874111103106
53%|█████▎ | 9/17 [10:15<08:56, 67.08s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.795 The correlation coefficient is: 0.943 The mean square error is: 0.05847706866111131
59%|█████▉ | 10/17 [11:25<07:55, 67.98s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.768 The correlation coefficient is: 0.914 The mean square error is: 0.05530045261979468
65%|██████▍ | 11/17 [12:35<06:51, 68.56s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.688 The correlation coefficient is: 0.887 The mean square error is: 0.07499811102953335
71%|███████ | 12/17 [13:46<05:45, 69.07s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.698 The correlation coefficient is: 0.886 The mean square error is: 0.08225232919417001
76%|███████▋ | 13/17 [14:56<04:37, 69.38s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.697 The correlation coefficient is: 0.908 The mean square error is: 0.09058791296148527
82%|████████▏ | 14/17 [16:05<03:28, 69.42s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.801 The correlation coefficient is: 0.935 The mean square error is: 0.0629141513700581
88%|████████▊ | 15/17 [17:14<02:18, 69.29s/it]
The amount of data points is 68931 The slope of the best fitting line is 0.749 The correlation coefficient is: 0.908 The mean square error is: 0.06267898382437069
94%|█████████▍| 16/17 [18:24<01:09, 69.34s/it]
The amount of data points is 70794 The slope of the best fitting line is 0.698 The correlation coefficient is: 0.897 The mean square error is: 0.0762321562720096
100%|██████████| 17/17 [19:33<00:00, 69.00s/it]
Other Years (Daily)¶
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r_all2 = np.array([])
rms_all2 = np.array([])
slope_all2 = np.array([])
for i in tqdm(range (0, len(ds.time_counter))):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, _ = datasets_preparation(dataset, dataset2)
r, rms, m = regressor3(drivers, diat)
r_all2 = np.append(r_all2,r)
rms_all2 = np.append(rms_all2,rms)
slope_all2 = np.append(slope_all2,m)
plotting2(r_all2, 'Correlation Coefficients')
plotting2(rms_all2, 'Root Mean Square Errors')
plotting2(slope_all2, 'Slope of the best fitting line')
100%|██████████| 640/640 [11:11:27<00:00, 62.95s/it]
Daily Maps¶
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maps = random.sample(range(0,len(ds.time_counter)),10)
for i in tqdm(maps):
dataset = ds.isel(time_counter=i)
dataset2 = ds2.isel(time_counter=i)
drivers, diat, indx = datasets_preparation(dataset, dataset2)
diat_i = dataset['Diatom']
regressor4(drivers, diat, 'Diatom ')
0%| | 0/10 [00:00<?, ?it/s]
The amount of data points is 1863 The slope of the best fitting line is 0.647 The correlation coefficient is: 0.897 The mean square error is: 0.10480773003649026
10%|█ | 1/10 [01:05<09:52, 65.80s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.606 The correlation coefficient is: 0.773 The mean square error is: 0.06227340657842682
20%|██ | 2/10 [02:11<08:45, 65.70s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.963 The correlation coefficient is: 0.853 The mean square error is: 0.03875772320600051
30%|███ | 3/10 [03:18<07:43, 66.24s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.451 The correlation coefficient is: 0.548 The mean square error is: 0.10725482913764549
40%|████ | 4/10 [04:27<06:43, 67.32s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.595 The correlation coefficient is: 0.854 The mean square error is: 0.07082650334617817
50%|█████ | 5/10 [05:35<05:37, 67.51s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.67 The correlation coefficient is: 0.796 The mean square error is: 0.06617496392247635
60%|██████ | 6/10 [06:44<04:31, 67.97s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.905 The correlation coefficient is: 0.921 The mean square error is: 0.02139780378862054
70%|███████ | 7/10 [07:55<03:26, 68.98s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.661 The correlation coefficient is: 0.865 The mean square error is: 0.06785533182710528
80%|████████ | 8/10 [09:02<02:16, 68.41s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.719 The correlation coefficient is: 0.856 The mean square error is: 0.06970611692619857
90%|█████████ | 9/10 [10:09<01:08, 68.08s/it]
The amount of data points is 1863 The slope of the best fitting line is 0.683 The correlation coefficient is: 0.902 The mean square error is: 0.07775188441965246
100%|██████████| 10/10 [11:17<00:00, 67.73s/it] 100%|██████████| 10/10 [11:17<00:00, 67.73s/it]
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